Advances in Electrochemical Sensors and Intelligent Systems for Food Quality and Waste Valorization

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 818

Special Issue Editors


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Guest Editor
Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, São Martinho do Bispo, 3045-093 Coimbra, Portugal
Interests: food quality control; food safety; electrochemical sensors; electronic tongues and noses; biowaste valorization; bioprocesses

E-Mail Website
Guest Editor
Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, São Martinho do Bispo, 3045-093 Coimbra, Portugal
Interests: water pollution; photocatalysis; analytical chemistry; food chemistry; biowaste valorization; waste-based nanomaterials

E-Mail Website
Guest Editor
Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, São Martinho do Bispo, 3045-093 Coimbra, Portugal
Interests: electrochemistry; applied technologies for food-related challenges; sustainability; waste/biowaste management; biowaste valorization

Special Issue Information

Dear Colleagues,

Ensuring food quality, safety, and sustainability remains a critical challenge for the global food sector. Recent advances in electrochemical sensing technologies, combined with data-driven and intelligent analytical methodologies, are reshaping current strategies for monitoring food composition, detecting contaminants, and managing biowaste as well as byproducts. These innovations not only aim to enhance analytical performance but also to reduce environmental and societal impacts while promoting economic valorization. Electrochemical sensors enable rapid, sensitive, and in situ detection of chemical and biological markers that are relevant to food quality and safety. In parallel, intelligent systems, such as machine learning algorithms and IoT-based platforms, provide powerful tools for real-time data interpretation, predictive modelling, and process optimization throughout food production and the supply chain.

This Special Issue, “Advances in Electrochemical Sensors and Intelligent Systems for Food Quality and Waste Valorization,” aims to gather high-quality, state-of-the-art research focused on the development, integration, and application of electrochemical sensing technologies and smart analytical systems in food-related contexts. Topics of interest include, but are not limited to, novel sensor materials, portable and wearable analytical devices, intelligent packaging solutions, computational and chemometric approaches, biowaste conversion strategies, and sustainable valorization technologies.

By bringing together contributions from diverse scientific and technological disciplines, this Special Issue aims to highlight recent breakthroughs, address existing challenges, and foster innovative perspectives toward safer, more resilient, and sustainable food systems.

Dr. Ana Cristina A. Veloso
Prof. Dr. Diana L.D. Lima
Dr. Ana S. Fajardo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrochemical sensors
  • electronic tongues
  • electronic noses
  • electronic eyes
  • thin-film sensor devices
  • image analysis
  • chemometrics
  • smart packaging
  • biowaste/byproduct valorization
  • IoT-based platforms

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Published Papers (1 paper)

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Research

24 pages, 2150 KB  
Article
Non-Destructive Freshness Assessment of Atlantic Salmon (Salmo salar) via Hyperspectral Imaging and an SPA-Enhanced Transformer Framework
by Zhongquan Jiang, Yu Li, Mincheng Xie, Hanye Zhang, Haiyan Zhang, Guangxin Yang, Peng Wang, Tao Yuan and Xiaosheng Shen
Foods 2026, 15(4), 725; https://doi.org/10.3390/foods15040725 - 15 Feb 2026
Viewed by 535
Abstract
Monitoring the freshness of Salmo salar within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of [...] Read more.
Monitoring the freshness of Salmo salar within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of modern industry. Here, we present a novel detection framework synergizing hyperspectral imaging (400–1000 nm) with the Transformer deep learning architecture. Through a rigorous comparative analysis of twelve preprocessing protocols and four feature wavelength selection algorithms (Lasso, Genetic Algorithm, Successive Projections Algorithm, and Random Frog), prediction models for Total Volatile Basic Nitrogen (TVB-N) and Total Viable Count (TVC) were established. Furthermore, the capacity of the Transformer to capture long-range spectral dependencies was systematically investigated. Experimental results demonstrate that the model integrating Savitzky-Golay (SG) smoothing with the Transformer yielded optimal performance across the full spectrum, achieving determination coefficients (R2) of 0.9716 and 0.9721 for the Prediction Sets of TVB-N and TVC, respectively. Following the extraction of 30 characteristic wavelengths via the Successive Projections Algorithm (SPA), the streamlined model retained exceptional predictive precision (R2 ≥ 0.95) while enhancing computational efficiency by a factor of approximately six. This study validates the superiority of attention-mechanism-based deep learning algorithms in hyperspectral data analysis. These findings provide a theoretical foundation and technical underpinning for the development of cost-effective, high-efficiency portable multispectral sensors, thereby facilitating the intelligent transformation of the aquatic product supply chain. Full article
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